Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription

• URL: http://arxiv.org/abs/2309.08454v2
• Date: Wed, 26 Feb 2025 15:28:38 GMT
• Title: Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription
• Authors: Peter Vieting, Simon Berger, Thilo von Neumann, Christoph Boeddeker, Ralf Schlüter, Reinhold Haeb-Umbach,
• Abstract summary: TF-GridNet has shown impressive performance in speech separation in real reverberant conditions.<n>We extend the mixture encoder from a static two-speaker scenario to a natural meeting context.<n>Experiments result in a new state-of-the-art performance on LibriCSS using a single microphone.
• Score: 31.774032625780414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-life applications of automatic speech recognition (ASR) require processing of overlapped speech. A common method involves first separating the speech into overlap-free streams on which ASR is performed. Recently, TF-GridNet has shown impressive performance in speech separation in real reverberant conditions. Furthermore, a mixture encoder was proposed that leverages the mixed speech to mitigate the effect of separation artifacts. In this work, we extended the mixture encoder from a static two-speaker scenario to a natural meeting context featuring an arbitrary number of speakers and varying degrees of overlap. We further demonstrate its limits by the integration with separators of varying strength including TF-GridNet. Our experiments result in a new state-of-the-art performance on LibriCSS using a single microphone. They show that TF-GridNet largely closes the gap between previous methods and oracle separation independent of mixture encoding. We further investigate the remaining potential for improvement.

Related papers

• Linear Time Complexity Conformers with SummaryMixing for Streaming Speech Recognition [15.302106458232878]
  SummaryMixing is a promising linear-time complexity alternative to self-attention for non-streaming speech recognition. This work extends SummaryMixing to a Conformer Transducer that works in both a streaming and an offline mode. It shows that this new linear-time complexity speech encoder outperforms self-attention in both scenarios.
  arXiv  Detail & Related papers  (2024-09-11T10:24:43Z)
• A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Speech Translation [48.84039953531355]
  We propose a novel non-autoregressive generation framework for simultaneous speech translation (NAST-S2X) NAST-S2X integrates speech-to-text and speech-to-speech tasks into a unified end-to-end framework. It achieves high-quality simultaneous interpretation within a delay of less than 3 seconds and provides a 28 times decoding speedup in offline generation.
  arXiv  Detail & Related papers  (2024-06-11T04:25:48Z)
• Speech collage: code-switched audio generation by collaging monolingual corpora [50.356820349870986]
  Speech Collage is a method that synthesizes CS data from monolingual corpora by splicing audio segments. We investigate the impact of generated data on speech recognition in two scenarios.
  arXiv  Detail & Related papers  (2023-09-27T14:17:53Z)
• TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition [51.565319173790314]
  TokenSplit is a sequence-to-sequence encoder-decoder model that uses the Transformer architecture. We show that our model achieves excellent performance in terms of separation, both with or without transcript conditioning. We also measure the automatic speech recognition (ASR) performance and provide audio samples of speech synthesis to demonstrate the additional utility of our model.
  arXiv  Detail & Related papers  (2023-08-21T01:52:01Z)
• Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
  We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes. Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
  arXiv  Detail & Related papers  (2023-08-14T08:19:24Z)
• Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition [52.11964238935099]
  An audio-visual multi-channel speech separation, dereverberation and recognition approach is proposed in this paper. Video input is consistently demonstrated in mask-based MVDR speech separation, DNN-WPE or spectral mapping (SpecM) based speech dereverberation front-end. Experiments were conducted on the mixture overlapped and reverberant speech data constructed using simulation or replay of the Oxford LRS2 dataset.
  arXiv  Detail & Related papers  (2023-07-06T10:50:46Z)
• Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator [42.8787280791491]
  Multi-talker overlapped speech poses a significant challenge for speech recognition and diarization. We propose a cost-effective method to convert a single-talker automatic speech recognition system into a multi-talker one. We incorporate a diarization branch into the Sidecar, allowing for unified modeling of both ASR and diarization with a negligible overhead of only 768 parameters.
  arXiv  Detail & Related papers  (2023-05-25T17:18:37Z)
• Multi-Dimensional and Multi-Scale Modeling for Speech Separation Optimized by Discriminative Learning [9.84949849886926]
  Intra-SE-Conformer and Inter-Transformer (ISCIT) for speech separation. New network SE-Conformer can model audio sequences in multiple dimensions and scales.
  arXiv  Detail & Related papers  (2023-03-07T08:53:20Z)
• Speaker Embedding-aware Neural Diarization: a Novel Framework for Overlapped Speech Diarization in the Meeting Scenario [51.5031673695118]
  We reformulate overlapped speech diarization as a single-label prediction problem. We propose the speaker embedding-aware neural diarization (SEND) system.
  arXiv  Detail & Related papers  (2022-03-18T06:40:39Z)
• Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem [65.25725367771075]
  This study demonstrates, for the first time, that the synthesis-based approach can also perform well on this problem. Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols. By utilizing the synthesis model with the input of discrete symbols, after the prediction of discrete symbol sequence, each target speech could be re-synthesized.
  arXiv  Detail & Related papers  (2021-12-17T08:35:40Z)
• Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation [73.1652905564163]
  We address the problem of separating individual speech signals from videos using audio-visual neural processing. Most conventional approaches utilize frame-wise matching criteria to extract shared information between co-occurring audio and video. We propose a cross-modal affinity network (CaffNet) that learns global correspondence as well as locally-varying affinities between audio and visual streams.
  arXiv  Detail & Related papers  (2021-03-25T15:39:12Z)
• Continuous Speech Separation with Ad Hoc Microphone Arrays [35.87274524040486]
  Speech separation has been shown effective for multi-talker speech recognition. In this paper, we extend this approach to continuous speech separation. Two methods are proposed to mitigate a speech problem during single talker segments.
  arXiv  Detail & Related papers  (2021-03-03T13:01:08Z)
• Audio-visual Multi-channel Recognition of Overlapped Speech [79.21950701506732]
  This paper presents an audio-visual multi-channel overlapped speech recognition system featuring tightly integrated separation front-end and recognition back-end. Experiments suggest that the proposed multi-channel AVSR system outperforms the baseline audio-only ASR system by up to 6.81% (26.83% relative) and 22.22% (56.87% relative) absolute word error rate (WER) reduction on overlapped speech constructed using either simulation or replaying of the lipreading sentence 2 dataset respectively.
  arXiv  Detail & Related papers  (2020-05-18T10:31:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.